This is the official implementation of Elaborative Rehearsal for Zero-shot Action Recognition (ICCV2021)

Overview

Elaborative Rehearsal for Zero-shot Action Recognition

This is an official implementation of:

Shizhe Chen and Dong Huang, Elaborative Rehearsal for Zero-shot Action Recognition, ICCV, 2021. Arxiv Version

Elaborating a new concept and relating it to known concepts, we reach the dawn of zero-shot action recognition models being comparable to supervised models trained on few samples.

New SOTA results are also achieved on the standard ZSAR benchmarks (Olympics, HMDB51, UCF101) as well as the first large scale ZSAR benchmak (we proposed) on the Kinetics database.
PWC PWC PWC PWC

Installation

git clone https://github.com/DeLightCMU/ElaborativeRehearsal.git
cd ElaborativeRehearsal
export PYTHONPATH=$(pwd):${PYTHONPATH}

pip install -r requirements.txt

# download pretrained models
bash scripts/download_premodels.sh

Zero-shot Action Recognition (ZSAR)

Extract Features in Video

  1. spatial-temporal features
bash scripts/extract_tsm_features.sh '0,1,2'
  1. object features
bash scripts/extract_object_features.sh '0,1,2'

ZSAR Training and Inference

  1. Baselines: DEVISE, ALE, SJE, DEM, ESZSL and GCN.
# mtype: devise, ale, sje, dem, eszsl
mtype=devise
CUDA_VISIBLE_DEVICES=0 python zeroshot/driver/zsl_baselines.py zeroshot/configs/zsl_baseline_${mtype}_config.yaml ${mtype} --is_train
CUDA_VISIBLE_DEVICES=0 python zeroshot/driver/zsl_baselines.py zeroshot/configs/zsl_baseline_${mtype}_config.yaml ${mtype} --eval_set tst
# evaluate other splits
ksplit=1
CUDA_VISIBLE_DEVICES=0 python zeroshot/driver/zsl_baselines_eval_splits.py zeroshot/configs/zsl_baseline_${mtype}_config.yaml ${mtype} ${ksplit}

# gcn
CUDA_VISIBLE_DEVICES=0 python zeroshot/driver/zsl_kgraphs.py zeroshot/configs/zsl_baseline_kgraph_config.yaml --is_train
CUDA_VISIBLE_DEVICES=0 python zeroshot/driver/zsl_kgraphs.py zeroshot/configs/zsl_baseline_kgraph_config.yaml --eval_set tst
  1. ER-ZSAR and ablations:
# TSM + ED class representation + AttnPool (2nd row in Table 4(b))
CUDA_VISIBLE_DEVICES=0 python zeroshot/driver/zsl_vse.py zeroshot/configs/zsl_vse_wordembed_config.yaml --is_train --resume_file datasets/Kinetics/zsl220/word.glove42b.th

# TSM + ED class representation + BERT (last row in Table 4(a) and Table 4(b))
CUDA_VISIBLE_DEVICES=0 python zeroshot/driver/zsl_vse.py zeroshot/configs/zsl_vse_config.yaml --is_train

# Obj + ED class representation + BERT + ER Loss (last row in Table 4(c))
CUDA_VISIBLE_DEVICES=0 python zeroshot/driver/zsl_cptembed.py zeroshot/configs/zsl_cpt_config.yaml --is_train

# ER-ZSAR Full Model
CUDA_VISIBLE_DEVICES=0 python zeroshot/driver/zsl_ervse.py zeroshot/configs/zsl_ervse_config.yaml --is_train

Citation

If you find this repository useful, please cite our paper:

@proceeding{ChenHuang2021ER,
  title={Elaborative Rehearsal for Zero-shot Action Recognition},
  author={Shizhe Chen and Dong Huang},
  booktitle = {ICCV},
  year={2021}
}

Acknowledgement

Owner
DeLightCMU
Research group at CMU
DeLightCMU
Implementation for the IJCAI2021 work "Beyond the Spectrum: Detecting Deepfakes via Re-synthesis"

Beyond the Spectrum Implementation for the IJCAI2021 work "Beyond the Spectrum: Detecting Deepfakes via Re-synthesis" by Yang He, Ning Yu, Margret Keu

Yang He 27 Jan 07, 2023
Supporting code for "Autoregressive neural-network wavefunctions for ab initio quantum chemistry".

naqs-for-quantum-chemistry This repository contains the codebase developed for the paper Autoregressive neural-network wavefunctions for ab initio qua

Tom Barrett 24 Dec 23, 2022
Rest API Written In Python To Classify NSFW Images.

Rest API Written In Python To Classify NSFW Images.

Wahyusaputra 2 Dec 23, 2021
Official Pytorch implementation of ICLR 2018 paper Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge.

Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge: Official Pytorch implementation of ICLR 2018 paper Deep Learning for Phy

emmanuel 47 Nov 06, 2022
an Evolutionary Algorithm assisted GAN

EvoGAN an Evolutionary Algorithm assisted GAN ckpts

3 Oct 09, 2022
ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers

ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers Official implementation of ViewFormer. ViewFormer is a NeRF-free neural rend

Jonáš Kulhánek 169 Dec 30, 2022
Contrastive Feature Loss for Image Prediction

Contrastive Feature Loss for Image Prediction We provide a PyTorch implementation of our contrastive feature loss presented in: Contrastive Feature Lo

Alex Andonian 44 Oct 05, 2022
Pytorch Geometric Tutorials

Pytorch Geometric Tutorials

Antonio Longa 648 Jan 08, 2023
Neural Ensemble Search for Performant and Calibrated Predictions

Neural Ensemble Search Introduction This repo contains the code accompanying the paper: Neural Ensemble Search for Performant and Calibrated Predictio

AutoML-Freiburg-Hannover 26 Dec 12, 2022
Modified prey-predator system - Modified prey–predator model describes the rate of change for each species by adding coupling terms.

Modified prey-predator system We aim to study the behaviors of the modified prey–predator model and establish the effects of several parameters that p

Seoyoung Oh 1 Jan 02, 2022
Streamlit component for TensorBoard, TensorFlow's visualization toolkit

streamlit-tensorboard This is a work-in-progress, providing a function to embed TensorBoard, TensorFlow's visualization toolkit, in Streamlit apps. In

Snehan Kekre 27 Nov 13, 2022
NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions (CVPR2021)

NExT-QA We reproduce some SOTA VideoQA methods to provide benchmark results for our NExT-QA dataset accepted to CVPR2021 (with 1 'Strong Accept' and 2

Junbin Xiao 50 Nov 24, 2022
The Easy-to-use Dialogue Response Selection Toolkit for Researchers

Easy-to-use toolkit for retrieval-based Chatbot Recent Activity Our released RRS corpus can be found here. Our released BERT-FP post-training checkpoi

GMFTBY 32 Nov 13, 2022
From Perceptron model to Deep Neural Network from scratch in Python.

Neural-Network-Basics Aim of this Repository: From Perceptron model to Deep Neural Network (from scratch) in Python. ** Currently working on a basic N

Aditya Kahol 1 Jan 14, 2022
Apply our monocular depth boosting to your own network!

MergeNet - Boost Your Own Depth Boost custom or edited monocular depth maps using MergeNet Input Original result After manual editing of base You can

Computational Photography Lab @ SFU 142 Dec 17, 2022
Code of Periodic Activation Functions Induce Stationarity

Periodic Activation Functions Induce Stationarity This repository is the official implementation of the methods in the publication: L. Meronen, M. Tra

AaltoML 12 Jun 07, 2022
Python implementation of Lightning-rod Agent, the Stack4Things board-side probe

Iotronic Lightning-rod Agent Python implementation of Lightning-rod Agent, the Stack4Things board-side probe. Free software: Apache 2.0 license Websit

2 May 19, 2022
[ICCV'21] UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction

UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction Project Page | Paper | Supplementary | Video This reposit

331 Dec 28, 2022
PyTorch Implement for Path Attention Graph Network

SPAGAN in PyTorch This is a PyTorch implementation of the paper "SPAGAN: Shortest Path Graph Attention Network" Prerequisites We prefer to create a ne

Yang Yiding 38 Dec 28, 2022
A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo

idn-solver Paper | Project Page This repository contains the code release of our ICCV 2021 paper: A Confidence-based Iterative Solver of Depths and Su

zhaowang 43 Nov 17, 2022